Decentralized optimization has attracted much research interest for resource-limited networked multi-agent systems in recent years. Decentralized consensus optimization, which is one of the decentralized optimization problems of great practical importance, minimizes an objective function that is the sum of the terms from individual agents over a set of variables on which all the agents should reach a consensus on. This problem can be reformulated into an equivalent model with two blocks of variables, which can then be solved by the Alternating Direction Method (ADM) with only communications between neighbor nodes.